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- W326410812 abstract "Abstract : TACTICAL AIRCRAFT MUST FREQUENTLY PERFORM COMPLEX SEQUENTIAL TASKS IN WHICH THEY RELY HEAVILY ON THE INTEGRATION OF SENSORY DATA TO ASSESS STATE AND MAINTAIN SITUATIONAL AWARENESS. IN MODERN SYSTEMS, THE CONTROL OF THE SENSORS' INFORMATION-GATHERING ACTIVITIES IS CRITICAL-OPTIMAL PERFORMANCE IS DESIRED. BUT THIS IS MADE DIFFICULT BY THE REQUIREMENTS TO CONTEND WITH SOPHISTICATED FLEXIBLE SENSORY ASSETS, AND VOLATILE, UNCERTAIN ENVIRONMENTS. THIS PAPER INTRODUCES THE SENSOR MANAGEMENT PROBLEM AND THE PLAUSIBILITY OF LEVERAGING A MACHINE LEARNING ALGORITHM TOWARD THIS DIFFICULT CHALLENGE." @default.
- W326410812 created "2016-06-24" @default.
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- W326410812 date "1996-10-01" @default.
- W326410812 modified "2023-09-27" @default.
- W326410812 title "Achieving Near-Optimal Sensor Allocation Policies Through Reinforcement Learning." @default.
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